P.088 Wounded glioma syndrome: neurologic worsening in patients with subtotal resection in high-grade gliomas
Bibliographic record
Abstract
Background: For treatment of high-grade gliomas (HGGs), subtotal resection (STR) may be preferred to minimize injury to eloquent areas. We aimed to characterize neurologic deficits developed in STR patients within the first month post-operatively and to establish a potential threshold for a safe volume of residual tumor to avoid neurological worsening. Methods: This is a single institution retrospective chart review, with 146 charts reviewed and 78 patients deemed eligible. Preoperative deficits and postoperative neurological deficits presenting prior to 1 month after surgery were captured. Imaging features such as tumour volume, edema, and other pertinent imaging characteristics were collected from preoperative and postoperative imaging. Results: Most patients that developed a postoperative deficit presented with motor deficits (55.1%), while only 1.3% of patients developed new or worsening tremor after surgery. On average, in patients with a new deficit, 26.5% of tumor was resected, and all patients had more than 19% of residual tumor. Conclusions: Postoperative neurologic deficits may develop after a subtotal resection when an average of 73.5% of tumor remains. The proposed threshold for tumor resection is greater than 26.5% to minimize the potential of neurologic worsening 1 month postoperatively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".